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Microsoft Analysis Services Training
We are glad to let you know that we are starting with Microsoft Analysis Services Training in the following topics. We will be publishing the tutorials as we complete. Please use discussion board if you have any questions and would like to discuss any. We ...
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- This tutorial starts with the introduction to Data Warehousing, Defination of OLAP, difference between Data warehouse and the OLTP Database, Objectives of data warehousing and data flow.
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- In this tutorial we will learn about the differences between Data Warehouse database and OLTP database and the objectives of a Data warehouse and Data flow.
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- This tutorial covers OLAP solutions used by Data warehouses and understanding Data Warehouse design. The enterprise needs to ask itself certain fundamental questions before actually launching on the process of designing the data warehouse. It must begin with a conviction that a data warehouse would ...
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- This tutorial covers the basic design concepts, The top down approach, The Bottom-Up Approach , Hybrid Approach and Federated approach.
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- This tutorial covers Designing the Dimensional Model, Dimensional Model schemas like Star Schema, Snowflake Schema, Optimizing star schema and Design of the Relational Database, OLAP Cubes and Data mining tools, Security considerations, metadata and backup and recovery plans.
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- This tutorial covers the different types of OLAP models like Relational Online Analytical Processing mode( ROLAP), Multidimensional Online Analytical processing mode(MOLAP) and Hybrid Online Analytical Processing mode or HOLAP.
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- This tutorial covers the types of changes that impact on cubes and Synchronization OLAP and data warehouse data.
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- This tutorial covers the download and step by step installation instructions along with the screen shots which helps with your PC setup to contine learning MSAS.
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- This tutorial covers brief introduction to Analysis Manager Wizards, how to start, Previewing and Defining terms which helps to understand the navigation along with the screen shots.
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- This tutorial explains the steps to create the cube and the preparation like settip the datasource.
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- This tutorial explains Setting up the Database in Analysis Server and how to connect and test the connectivity.
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- This three part tutorial explains about cubes, Dimensions and how to create them using the wizards. You need to understand and learn how to create the cubes since it's very critical step.
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- This is part 2 in MSAS : Building the Cube series, If you have not completed reading part1, please do so before continuing with this tutorial.
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- This is part 3 in MSAS : Building the Cube series, If you have not completed reading part 1 and 2 , please do so before continuing with this tutorial.
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- This tutorial covers the storage modes for the cube before it can be processed. The kind of storage selected will determine the query performance and enhance the cube efficiency.
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- This tutorial explains about the Cube Browser, it's a tool provided within the Analysis Services to display the results of the cube process without the need to add external software.
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- This tutorial explains various functions available and the tools available for building and managing data warehouses.
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- The Data warehousing framework is a set of components and API's that implement the data warehousing features of the SQL server 2000. The common interface of the server known as the Enterprise Manager can be used by various components to build and use the data warehouse or a data mart.
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- This is part 2 of MSAS : The Data warehousing framework of SQL Server 2000. It's very important that you understand the concepts if you are really trying to get job in Data Warehousing field.
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- Analysis services is the middle tier server component of the MS SQL 2000. It manages multidimensional OLAP cubes of data and ease of access to such data to end users. It also enables user to create data mining models from the data in the cubes or in the relational databases.
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- The primary aim of Microsoft was to ease the process of building and using data warehouses. A large number of wizards, editors and tools have been built into the Analysis services to fulfill this objective. The wizards available for use are: The Cube wizard that helps the user build all the structur...
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- Analysis services provides various tools that can be programmatically used to extend its functionality. Analysis services supports OLE DB to meet the OLAP specific requirements and is also designed to meet the requirements for Data mining specifications. The Analysis services is made compatible with...
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- Metadata is a description of data. It provides a consistent way to describe data structures. It is used to describe data as it is being transformed and gives a clear explanation of the meaning of different fields, measures, levels and dimensions. All releases of Microsoft development and data manage...
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- In the first tutorial of this series “Introduction to Data Warehousing and OLAP” we briefly touched upon storage options that are used in data warehousing. In the second chapter “ Introducing Analysis Manager Wizards” we learnt how to use the cube Storage wizard to set storag...
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- The PivotTable Service:The interfaces used by client applications to access OLAP data and data mining data on the server are provided by the PivotTable Service. The PivotTable service is a set of tools that enable the transfer of OLAP cubes to client applications from the OLAP server. Two programmin...
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- PivotTable component in Office 2000 is the PivotTable report feature. It allows the user select and cross tabulate numerical values in ways that are similar to the processes of a cube. The earlier versions of this report in Excel could only extract values from relational data sources. The version un...
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- The next 6 tutorials explains Building Dimensions using Dimensions Editor. Dimensional modeling is the conceptualization and visualization of numerical data models as a set of measures that are derived from the common parameters used in a business. It summarizes and rearranges data and presents view...
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- This tutorial explains how to create Shared dimension using Dimension Editor , Private dimension using Dimension Editor and the differences between them.
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- Dimensions are created, based on dimension table columns, member properties, or from the structure of OLAP data mining models. When a dimension is defined, there are a number of possible approaches. Each approach produces a different dimension variety. Standard Dimensions are regular dimensions. The...
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- A level is an element of a dimension hierarchy that describes the hierarchy from the highest level to the lowest level of data. Levels exist within dimensions and are based on columns in the dimension table or member properties in the dimension. They specify the contents and structure of the dimensi...
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- Parent child dimensions when viewed from within a cube reveal some interesting features. We will add an private employee parent child dimension to the sales cube and study the features thereof.
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- Dimensions are defined as structural attributes of a cube made up of levels arranged in hierarchies. A level is a set of members of a dimension organized such that all members of the set are at an equal distance from the root of the hierarchy. A hierarchy is the set of members in a dimension and the...
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- Dimensions are stored in the Multidimensional OLAP or Relational OLAP. The storage mode determines the location and form of the dimensions data. While MOLAP stores data in a multidimensional structure on the Analysis server, ROLAP stores the data in the relational tables. The storage mode can be set...
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- A hierarchy defines the relative positions of members in a dimension. Hierarchies are sometimes represented as pyramidal structures. The members in this structure are arranged in an expansive order—from the most summarized to the most detailed. For instance in a geography dimension the country...
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- Time dimensions are part and parcel of OLAP cubes. At the lowest level of detail a time dimension may contain a month, minute or even a second. At the most summarized level it may contain a year, a decade or a century. The repetitive nature of time encourages users to view data in terms of a time di...
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- To enable proper aggregation of values along a dimension each member of the dimension needs its own aggregation rule. These rules are provided by custom roll up. Custom rollup operators provide a simple way of controlling the process of rolling up a member to its parents values. Custom rollup operat...
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- Member properties are attributes associated with members. They contain some additional information about a member but cannot be used to create a level in the dimension by themselves. For example each member of the Month level has an associated Boolean number property called Bonus month.
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- A logical dimension created out of the columns of a physical dimension is a virtual dimension. The contents of a virtual dimension are member properties of the physical dimension or columns and tables of a physical dimension. For instance the Store name level of the Store dimension has a member prop...
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- The main object in Online Analytical processing is the cube. A cube is defined as a subset of the data in the data warehouse, organized and summarized into multidimensional structure that consists of dimensions and measures.
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- The quantitative and numerical columns from a fact table of a cube are the measures of the cube. When the cube is processed the data in the measures get aggregated across the dimensions of the cube. These measures are of primary interest to the end user and are the central values that get analyzed i...
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- Each object in the properties pane of the Cube Editor has its own set of properties which can be used to modify the settings of the object. The properties of a cube defined in the properties pane are as tabulated below. In this section we shall use these properties to set the properties of our cub...
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- Online Analytical Processing (OLAP) is essentially data presented as Cubes, dimensions, hierarchies and measures. Users can navigate a complex set of data intuitively using these objects. In this context, consistent response times for each view or slice of data become important. Therefore modes of s...
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- The Storage Design Wizard helps the user set the storage options and design the aggregations for a cube. While choosing from the three available storage modes the user has to keep certain factors in mind.
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- Pre-calculated summaries of data that improve query response time are called aggregations. OLAP technology organizes data into the multidimensional structures of cubes. The dimensions and their hierarchies define the queries that can be asked of the cubes.
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- This tutorial explains the usage of the Usage Based Optimization Wizard, The Server Cache , Query Usage and Manipulating the query log.
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- SQL Server 2000 Analysis services comes with certain features that optimize performance without the intervention of the Administrator. The Storage Engine is optimized by incorporating the following features;
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- The SQL Server Query Analyzer is a feature rich Query Analyzer. The tool can provide a graphical representation of a query and helps the user mould queries into more efficient shapes. It also helps identify faulty queries. +To Tune the index created the user will have to open the SQL query analyzer ...
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- The Analysis server is like a black box. Everything that happens in the server is recorded, but the end user sees a multidimensional cube created out of the data in the data warehouse and has very little taste of what really goes on behind the scenes. In this section we will be examining this black ...
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- As stated earlier, maps are created when a dimension is processed. However, existing maps of a dimension are destroyed when it is processed and new maps are created. Consequently all cubes accessing the dimension will find it inaccessible and the cube will be invalid. The dimension will become acces...
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- Cubes are made up of a fact table and several dimensions. Optimizing cube processing therefore, involves optimizing dimensions also. Let us look at two scenarios. One in which the dimensions are not optimized and the cube is processed and another in which the dimensions are optimized and the cube...
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